A Block Arnoldi Method for the SPN Equations

Author(s):  
A. Vidal-Ferràndiz ◽  
A. Carreño ◽  
D. Ginestar ◽  
G. Verdú
Keyword(s):  
2014 ◽  
Vol 06 (06) ◽  
pp. 1450069 ◽  
Author(s):  
QIANG ZHOU ◽  
GANG CHEN ◽  
YUEMING LI

A reduced-order model (ROM) based on block Arnoldi algorithm to quickly predict flutter boundary of aeroelastic system is investigated. First, a mass–damper–spring dynamic system is tested, which shows that the low dimension system produced by the block Arnoldi method can keep a good dynamic property with the original system in low and high frequencies. Then a two-degree of freedom transonic nonlinear aerofoil aeroelastic system is used to validate the suitability of the block Arnoldi method in flutter prediction analysis. In the aerofoil case, the ROM based on a linearized model is obtained through a high-fidelity nonlinear computational fluid dynamics (CFD) calculation. The order of the reduced model is only 8 while it still has nearly the same accuracy as the full 9600-order model. Compared with the proper orthogonal decomposition (POD) method, the results show that, without snapshots the block Arnoldi/ROM has a unique superiority by maintaining the system stability aspect. The flutter boundary of the aeroelastic system predicted by the block Arnoldi/ROM agrees well with the CFD and reference results. The Arnoldi/ROM provides an efficient and convenient tool to quick analyze the system stability of nonlinear transonic aeroelastic systems.


2019 ◽  
Vol 78 (8) ◽  
pp. 2817-2830
Author(s):  
I. Abdaoui ◽  
L. Elbouyahyaoui ◽  
M. Heyouni

2011 ◽  
Vol 20 (2) ◽  
pp. 208-219 ◽  
Author(s):  
A. Bouhamidi ◽  
M. Hached ◽  
M. Heyouni ◽  
K. Jbilou

2003 ◽  
Vol 150 (1) ◽  
pp. 23 ◽  
Author(s):  
B. Lee ◽  
H. Song ◽  
S.-H. Kwon ◽  
D. Kim ◽  
K. Iba ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1522
Author(s):  
Anna Concas ◽  
Lothar Reichel ◽  
Giuseppe Rodriguez ◽  
Yunzi Zhang

The power method is commonly applied to compute the Perron vector of large adjacency matrices. Blondel et al. [SIAM Rev. 46, 2004] investigated its performance when the adjacency matrix has multiple eigenvalues of the same magnitude. It is well known that the Lanczos method typically requires fewer iterations than the power method to determine eigenvectors with the desired accuracy. However, the Lanczos method demands more computer storage, which may make it impractical to apply to very large problems. The present paper adapts the analysis by Blondel et al. to the Lanczos and restarted Lanczos methods. The restarted methods are found to yield fast convergence and to require less computer storage than the Lanczos method. Computed examples illustrate the theory presented. Applications of the Arnoldi method are also discussed.


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